Functional principal component data analysis: a new method for analysing microbial community fingerprints

Effects of Different Native Plants on Soil Remediation and Microbial Diversity in Jiulong Iron Tailings Area, Jiangxi

Phytoremediation is an important solution to heavy metal pollution in soil. However, the impact of plants on microbial communities in contaminated soil also requires attention. Community-level physiological profiling (CLPP) based on the Biolog™ EcoPlate and high-throughput sequencing were used to study the soil microbial community in this article. The rhizosphere and bulk soil samples of six native species were collected from the iron mine tailings on Jiulong Mountain, Jiangxi Province. According to the average well color development (AWCD), all plants improved the activity and diversity of the contaminated soil microbial community to varying degrees. Cunninghamia lanceolate is considered to have good effects and led to the appearance of Cunninghamia lanceolata > Zelkova schneideriana > Toona ciliata > Alnus cremastogyne > Cyclobalanopsis myrsinifolia > Pinus elliottii. The Shannon–Wiener diversity index and principal component analysis (PCA) show that the evenness and dominance of soil microbial communities of several plants are structurally similar to those of uncontaminated soil (UNS). The results of high-throughput sequencing indicated that the bacterial community diversity of C. lanceolata, A. cremastogyne, and P. elliottii is similar to UNS, while fungal community diversity is different from UNS. C. lanceolata has a better effect on soil nutrients, C. myrsinifolia and P. elliottii may have a better effect on decreasing the Cu content. The objective of this study was to assess the influence of native plants on microbial communities in soils and the soil remediation capacity. Mortierellomycota was the key species for native plants to regulate Cu and microbial community functions. Native plants have decisive influence on microbial community diversity.

Soil microbial functional diversity responses to different vegetation types in the Heilongjiang Zhongyangzhan Black-billed Capercaillie Nature Reserve

Purpose

The soil microbial community is an important bioactive component of terrestrial ecosystems. Its structural and functional diversity directly affects carbon and nitrogen processes. This study aimed to investigate the variations in the functional diversity of soil microbial communities in forests with different types of vegetation.

Methods

We selected three typical vegetation types, larch (LG), black birch (BD), and larch and black birch mixed (LGBD) forests, located in the Heilongjiang Zhongyangzhan Black-billed Capercaillie Nature Reserve. The Biolog-Eco microplate technology was selected to perform these analyses.

Result

Our results showed clear differences between microorganisms in the three typical forests. The average well colour development (AWCD) change rate gradually increased with incubation time. The BD type had the highest AWCD value, followed by LGBD; the LG forest type had the lowest value. The difference in the soil microbial alpha diversity index between BD and LG was significant. A principal component analysis showed that PC1 and PC2 respectively explained 62.77% and 13.3% of the variance observed. The differences in the soil microbial carbon-source utilisation patterns under different vegetation types were mainly caused by esters and carbohydrates. Redundancy analysis showed that soil microbial functional diversity was strongly affected by soil physicochemistrical properties (e.g. organic carbon, total nitrogen and pH).

Conclusion

These results provide a reference for further exploring the relationship between forest communities and soil microbes during the process of forest succession.

Land application of sewage sludge: Response of soil microbial communities and potential spread of antibiotic resistance

The effect of land application of sewage sludge on soil microbial communities and the possible spread of antibiotic- and metal-resistant strains and resistance determinants were evaluated during a 720-day field experiment. Enzyme activities, the number of oligotrophic bacteria, the total number of bacteria (qPCR), functional diversity (BIOLOG) and genetic diversity (DGGE) were established. Antibiotic and metal resistance genes (ARGs, MRGs) were assessed, and the number of cultivable antibiotic- (ampicillin, tetracycline) and heavy metal- (Cd, Zn, Cu, Ni) resistant bacteria were monitored during the experiment. The application of 10 t ha^-1 of sewage sludge to soil did not increase the organic matter content and caused only a temporary increase in the number of bacteria, as well as in the functional and structural biodiversity. In contrast to expectations, a general adverse effect on the tested microbial parameters was observed in the fertilized soil. The field experiment revealed a significant reduction in the activities of alkaline and acid phosphatases, urease and nitrification potential. Although sewage sludge was identified as the source of several ARGs and MRGs, these genes were not detected in the fertilized soil. The obtained results indicate that the effect of fertilization based on the recommended dose of sewage sludge was not achieved.

Analysis on Metabolic Functions of Stored Rice Microbial Communities by BIOLOG ECO Microplates

Microbial contamination has been a pervasive issue during the rice storage and triggers extensive researches. The metabolism of microorganisms was proved as an indicator to mirror the degree of microbial contamination. It is necessary to develop a scientific method to analyze the metabolism of rice microbial communities, thereby monitoring the microbial contamination. In this study, the metabolism of rice microbial communities in different storing-year were investigated by BIOLOG ECO microplates. The three rice samples were respectively stored for 1-3 years. The related indicators of BIOLOG ECO microplates were determined, including average well-color development (AWCD) of carbon sources and three metabolic functional diversity indices. The results showed that there were significant differences in the AWCD of all carbon sources among the three rice microbial communities (p < 0.05), and the functional diversity indices except Simpson index showed significant differences (p < 0.05). Additionally, the three rice microbial communities differed significantly in the metabolic utilization of carboxylic acids and miscellaneous (p < 0.05), and there were, however, no significant differences in the other four types of carbon sources. Furthermore, principal component analysis revealed that the microbial communities of stored rice had obviously different metabolic functions in different storage period. Therefore, the study indicated that the BIOLOG ECO microplate was applicable to evaluate the metabolic functions of rice microbial communities, and carboxylic acids and miscellaneous were two crucial parameters of carbon sources to identify the metabolic differences of microbial communities, a case in which it reflected the conditions of rice microbial contamination.

Applying Functional Data Analysis to Assess Tele-Interpersonal Psychotherapy's Efficacy to Reduce Depression

The use of parametric linear mixed models and generalized linear mixed models to analyze longitudinal data collected during randomized control trials (RCT) is conventional. The application of these methods, however, is restricted due to various assumptions required by these models. When the number of observations per subject is sufficiently large, and individual trajectories are noisy, functional data analysis (FDA) methods serve as an alternative to parametric longitudinal data analysis techniques. However, the use of FDA in randomized control trials, is rare. In this paper, the effectiveness of FDA and linear mixed models was compared by analyzing data from rural persons living with HIV and comorbid depression enrolled in a depression treatment randomized clinical trial. Interactive voice response (IVR) systems were used for weekly administrations of the 10-item Self-Administered Depression Scale (SADS) over 41 weeks. Functional principal component analysis and functional regression analysis methods detected a statistically significant difference in SADS between telphone-administered interpersonal psychotherapy (tele-IPT) and controls but, linear mixed effects model results did not. Additional simulation studies were conducted to compare FDA and linear mixed models under a different nonlinear trajectory assumption. In this clinical trial with sufficient per subject measured outcomes and individual trajectories that are noisy and nonlinear, we found functional data analysis methods to be a better alternative to linear mixed models.

Characterizing early child growth patterns of height-for-age in an urban slum cohort of Bangladesh with functional principal component analysis

Background

Early childhood is a critical stage of physical and cognitive growth that forms the foundation of future wellbeing. Stunted growth is presented in one of every 4 children worldwide and contributes to developmental impairment and under-five mortality. Better understanding of early growth patterns should allow for early detection and intervention in malnutrition. We aimed to characterize early child growth patterns and quantify the change of growth curves from the World Health Organization (WHO) Child Growth Standards.

Methods

In a cohort of 626 Bangladesh children, longitudinal height-for-age z-scores (HAZ) were modelled over the first 24 months of life using functional principal component analysis (FPCA). Deviation of individual growth from the WHO standards was quantified based on the leading functional principal components (FPCs), and growth faltering was detected as it occurred. The risk factors associated with growth faltering were identified in a linear regression.

Results

Ninety-eight percent of temporal variation in growth trajectories over the first 24 months of life was captured by two leading FPCs (FPC1 for overall growth and FPC2 for change in growth trajectory). A derived index, adj-FPC2, quantified the change in growth trajectory (i.e., growth faltering) relative to the WHO standards. In addition to HAZ at birth, significant risk factors associated with growth faltering in boys included duration of breastfeeding, family size and income and in girls maternal weight and water source.

Conclusions

The underlying growth patterns of HAZ in the first 2 years of life were delineated with FPCA, and the deviations from the WHO standards were quantified from the two leading FPCs. The adj-FPC2 score provided a meaningful measure of growth faltering in the first 2 years of life, which enabled us to identify the risk factors associated with poor growth that would have otherwise been missed. Understanding faltering patterns and associated risk factors are important in the development of effective intervention strategies to improve childhood growth globally.

Trial registration

ClinicalTrials.gov Identifier: NCT02734264, registered 22 March, 2016.

Electronic supplementary material

The online version of this article (doi:10.1186/s12887-017-0831-y) contains supplementary material, which is available to authorized users.

Effect of redox conditions on bacterial community structure in Baltic Sea sediments with contrasting phosphorus fluxes

Phosphorus release from sediments can exacerbate the effect of eutrophication in coastal marine ecosystems. The flux of phosphorus from marine sediments to the overlying water is highly dependent on the redox conditions at the sediment-water interface. Bacteria are key players in the biological processes that release or retain phosphorus in marine sediments. To gain more insight in the role of bacteria in phosphorus release from sediments, we assessed the effect of redox conditions on the structure of bacterial communities. To do so, we incubated surface sediments from four sampling sites in the Baltic Sea under oxic and anoxic conditions and analyzed the fingerprints of the bacterial community structures in these incubations and the original sediments. This paper describes the effects of redox conditions, sampling station, and sample type (DNA, RNA, or whole-cell sample) on bacterial community structure in sediments. Redox conditions explained only 5% of the variance in community structure, and bacterial communities from contrasting redox conditions showed considerable overlap. We conclude that benthic bacterial communities cannot be classified as being typical for oxic or anoxic conditions based on community structure fingerprints. Our results suggest that the overall structure of the benthic bacterial community has only a limited impact on benthic phosphate fluxes in the Baltic Sea.

Applications of functional data analysis: A systematic review

BACKGROUND

Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods.

METHODS

A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995-2010. Papers reporting methodological considerations only were excluded, as were non-English articles.

RESULTS

In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data.

CONCLUSIONS

Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.